Where Am I? What Just Happened?
You may have started this book with a rather ordinary set of skills in math and spreadsheet modeling, but if you're here, having made it through alive (and having not just skipped the first 10 chapters), then I imagine you're now a spreadsheet modeling connoisseur with a good grasp of a variety of data science techniques.
This book has covered topics ranging from classic operations research fodder (optimization, Monte Carlo, and forecasting) to unsupervised learning (outlier detection, clustering, and graphs) to supervised AI (regression, decision stumps, and naïve Bayes). You should feel confident working with spreadsheet data at this higher level.
I also hope that Chapter 10 showed you that now that you understand data science techniques and algorithms, it's quite easy to use those techniques from within a programming language such as R.
And if there's a particular topic that really grabbed you in this book, dive deeper! Want more R, more optimization, more machine learning? Grab one of the sources I recommend in each relevant chapter's conclusion and read on. There's so much to learn. I've only scraped the surface of analytics practice in this book.
Before You Go-Go
I want to use this conclusion to offer up some thoughts about what it means to practice data science in the real world, because merely knowing the math isn't enough.
Anyone who knows me well knows that I'm not the sharpest knife in the drawer. My quantitative ...